Spiking Neural Networks (SNNs) have emerged as energy-efficient alternatives to Artificial
Neural Networks (ANNs) when deployed on neuromorphic chips. While recent studies have …

With the rapid development of deep learning, Deep Spiking Neural Networks (DSNNs) have
emerged as promising due to their unique spike event processing and asynchronous …

There is a growing necessity for edge training to adapt to dynamically changing
environment. Neuromorphic computing represents a significant pathway for high-efficiency …

Heart disease is one of the leading causes of death worldwide. Given its high risk and often
asymptomatic nature, real-time continuous monitoring is essential. Unlike traditional artificial …

Spiking neural networks (SNNs) use discrete binary spikes to transfer information between
neurons, which is different from artificial neural networks (ANNs). Although event-based …

With the development of deep learning models, there has been growing research interest in
spiking neural networks (SNNs) due to their energy efficiency resulting from their multiplier …

With the ever-growing need for artificial intelligent applications, the demand for energy-
efficient neural networks is more critical than ever given the significant environmental and …

Spiking neural networks (SNNs), inspired by biological processes, use spike signals for inter-
layer communication, presenting an energy-efficient alternative to traditional neural …